Detection of DDoS Attack in Cloud Computing using Machine Learning Algorithm

Abstract

Cloud Computing is highly susceptible to Distributed Denial of Service (DDoS) attacks, which can disrupt services and compromise security. Traditional methods struggle to detect evolving attack patterns effectively. This study explores machine learning algorithms like SVM, Random Forest, and Neural Networks for identifying DDoS attacks in real time. These models enhance accuracy and response time by distinguishing malicious traffic from legitimate users. The results highlight the effectiveness of intelligent threat detection in securing cloud environments.